mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 00:21:07 +01:00
939877bf4b
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14407 Reviewed By: yinghai Differential Revision: D13364364 Pulled By: wesolwsk fbshipit-source-id: e69bcd1bc52e35b2f0e45e5dc40184f1bd66605d
265 lines
8.0 KiB
Python
265 lines
8.0 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import unittest
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from functools import reduce
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import hypothesis.strategies as st
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from hypothesis import given, settings
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import numpy as np
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from caffe2.python import core, workspace
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import caffe2.python.hypothesis_test_util as hu
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import caffe2.python.ideep_test_util as mu
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@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
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class FcTest(hu.HypothesisTestCase):
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@given(n=st.integers(1, 5), m=st.integers(1, 5),
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k=st.integers(1, 5), **mu.gcs)
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def test_fc_2_dims(self, n, m, k, gc, dc):
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X = np.random.rand(m, k).astype(np.float32) - 0.5
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W = np.random.rand(n, k).astype(np.float32) - 0.5
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b = np.random.rand(n).astype(np.float32) - 0.5
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op = core.CreateOperator(
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'FC',
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['X', 'W', 'b'],
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["Y"]
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)
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self.assertDeviceChecks(dc, op, [X, W, b], [0])
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for i in range(3):
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self.assertGradientChecks(gc, op, [X, W, b], i, [0])
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@given(n=st.integers(1, 5),
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m=st.integers(1, 5),
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c=st.integers(1, 5),
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h=st.integers(1, 5),
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w=st.integers(1, 5),
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axis=st.integers(1, 3),
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**mu.gcs)
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def test_fc_with_axis(self, n, m, c, h, w, axis, gc, dc):
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X = np.random.rand(n, c, h, w).astype(np.float32) - 0.5
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k = reduce((lambda x, y: x * y), [n, c, h, w][axis - 4:])
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nn = reduce((lambda x, y: x * y), [n, c, h, w][:axis])
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W = np.random.rand(m, k).astype(np.float32) - 0.5
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b = np.random.rand(m).astype(np.float32) - 0.5
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dY = np.random.rand(nn, m).astype(np.float32) - 0.5
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op0 = core.CreateOperator(
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'FC',
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['X', 'W', 'b'],
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["Y"],
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axis=axis,
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device_option=dc[0]
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)
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op0_bw = core.CreateOperator(
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'FCGradient',
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['X', 'W', 'dY'],
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["dW", "db"],
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axis=axis,
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device_option=dc[0]
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)
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workspace.ResetWorkspace()
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workspace.FeedBlob('X', X, dc[0])
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workspace.FeedBlob('W', W, dc[0])
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workspace.FeedBlob('b', b, dc[0])
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workspace.RunOperatorOnce(op0)
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Y0 = workspace.FetchBlob('Y')
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workspace.FeedBlob('dY', dY, dc[0])
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workspace.RunOperatorOnce(op0_bw)
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dW0 = workspace.FetchBlob('dW')
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db0 = workspace.FetchBlob('db')
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op1 = core.CreateOperator(
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'FC',
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['X', 'W', 'b'],
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["Y"],
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axis=axis,
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device_option=dc[1]
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)
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op1_bw = core.CreateOperator(
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'FCGradient',
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['X', 'W', 'dY'],
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["dW", "db"],
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axis=axis,
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device_option=dc[1]
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)
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workspace.SwitchWorkspace("_device_check_", True)
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workspace.FeedBlob('X', X, dc[1])
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workspace.FeedBlob('W', W, dc[1])
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workspace.FeedBlob('b', b, dc[1])
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workspace.RunOperatorOnce(op1)
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Y1 = workspace.FetchBlob('Y')
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workspace.FeedBlob('dY', dY, dc[1])
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workspace.RunOperatorOnce(op1_bw)
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dW1 = workspace.FetchBlob('dW')
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db1 = workspace.FetchBlob('db')
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Y0 = Y0.flatten()
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Y1 = Y1.flatten()
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if not np.allclose(Y0, Y1, atol=0.01, rtol=0.01):
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print(Y1)
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print(Y0)
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print(np.max(np.abs(Y1 - Y0)))
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self.assertTrue(False)
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dW0 = dW0.flatten()
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dW1 = dW1.flatten()
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if not np.allclose(dW0, dW1, atol=0.01, rtol=0.01):
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print(dW1)
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print(dW0)
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print(np.max(np.abs(dW1 - dW0)))
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self.assertTrue(False)
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db0 = db0.flatten()
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db1 = db1.flatten()
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if not np.allclose(db0, db1, atol=0.01, rtol=0.01):
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print(db1)
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print(db0)
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print(np.max(np.abs(db1 - db0)))
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self.assertTrue(False)
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@given(n=st.integers(1, 5),
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o=st.integers(1, 5),
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i=st.integers(1, 5),
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h=st.integers(1, 5),
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w=st.integers(1, 5),
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axis_w=st.integers(1, 3),
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**mu.gcs)
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def test_fc_with_axis_w(self, n, o, i, h, w, axis_w, gc, dc):
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W = np.random.rand(o, i, h, w).astype(np.float32) - 0.5
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k = reduce((lambda x, y: x * y), [o, i, h, w][axis_w - 4:])
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m = reduce((lambda x, y: x * y), [o, i, h, w][:axis_w])
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X = np.random.rand(n, k).astype(np.float32) - 0.5
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b = np.random.rand(m).astype(np.float32) - 0.5
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dY = np.random.rand(n, m).astype(np.float32) - 0.5
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op0 = core.CreateOperator(
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'FC',
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['X', 'W', 'b'],
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["Y"],
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axis_w=axis_w,
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device_option=dc[0]
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)
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op0_bw = core.CreateOperator(
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'FCGradient',
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['X', 'W', 'dY'],
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["dW", "db"],
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axis_w=axis_w,
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device_option=dc[0]
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)
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workspace.ResetWorkspace()
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workspace.FeedBlob('X', X, dc[0])
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workspace.FeedBlob('W', W, dc[0])
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workspace.FeedBlob('b', b, dc[0])
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workspace.RunOperatorOnce(op0)
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Y0 = workspace.FetchBlob('Y')
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workspace.FeedBlob('dY', dY, dc[0])
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workspace.RunOperatorOnce(op0_bw)
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dW0 = workspace.FetchBlob('dW')
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db0 = workspace.FetchBlob('db')
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op1 = core.CreateOperator(
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'FC',
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['X', 'W', 'b'],
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["Y"],
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axis_w=axis_w,
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device_option=dc[1]
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)
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op1_bw = core.CreateOperator(
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'FCGradient',
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['X', 'W', 'dY'],
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["dW", "db"],
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axis_w=axis_w,
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device_option=dc[1]
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)
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workspace.SwitchWorkspace("_device_check_", True)
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workspace.FeedBlob('X', X, dc[1])
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workspace.FeedBlob('W', W, dc[1])
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workspace.FeedBlob('b', b, dc[1])
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workspace.RunOperatorOnce(op1)
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Y1 = workspace.FetchBlob('Y')
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workspace.FeedBlob('dY', dY, dc[1])
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workspace.RunOperatorOnce(op1_bw)
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dW1 = workspace.FetchBlob('dW')
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db1 = workspace.FetchBlob('db')
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Y0 = Y0.flatten()
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Y1 = Y1.flatten()
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if not np.allclose(Y0, Y1, atol=0.01, rtol=0.01):
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print(Y1)
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print(Y0)
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print(np.max(np.abs(Y1 - Y0)))
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self.assertTrue(False)
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dW0 = dW0.flatten()
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dW1 = dW1.flatten()
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if not np.allclose(dW0, dW1, atol=0.01, rtol=0.01):
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print(dW1)
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print(dW0)
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print(np.max(np.abs(dW1 - dW0)))
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self.assertTrue(False)
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db0 = db0.flatten()
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db1 = db1.flatten()
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if not np.allclose(db0, db1, atol=0.01, rtol=0.01):
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print(db1)
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print(db0)
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print(np.max(np.abs(db1 - db0)))
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self.assertTrue(False)
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@given(n=st.integers(1, 5), m=st.integers(1, 5),
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k=st.integers(1, 5), **mu.gcs)
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def test_fc_4_dims_src(self, n, m, k, gc, dc):
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X = np.random.rand(m, k, m, m).astype(np.float32) - 0.5
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W = np.random.rand(n, k * m * m).astype(np.float32) - 0.5
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b = np.random.rand(n).astype(np.float32) - 0.5
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op = core.CreateOperator(
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'FC',
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['X', 'W', 'b'],
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["Y"]
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)
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self.assertDeviceChecks(dc, op, [X, W, b], [0])
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for i in range(3):
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self.assertGradientChecks(gc, op, [X, W, b], i, [0])
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@given(n=st.integers(1, 5), m=st.integers(1, 5),
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k=st.integers(1, 5), **mu.gcs)
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def test_fc_4_dims(self, n, m, k, gc, dc):
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X = np.random.rand(m, k, m, m).astype(np.float32) - 0.5
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W = np.random.rand(n, k, m, m).astype(np.float32) - 0.5
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b = np.random.rand(n).astype(np.float32) - 0.5
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op = core.CreateOperator(
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'FC',
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['X', 'W', 'b'],
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["Y"]
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)
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self.assertDeviceChecks(dc, op, [X, W, b], [0])
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for i in range(3):
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self.assertGradientChecks(gc, op, [X, W, b], i, [0])
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if __name__ == "__main__":
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unittest.main()
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